The category that eats the chart

queue on support-tickets has 5 values, and one of them is 32.1% of the data.

• technical — 1,282 rows, 32.1%
• billing — 960 rows, 24.0%
• account — 647 rows, 16.2%
• onboarding — 586 rows, 14.6%
• integrations — 525 rows, 13.1%

The top three take 72.2% between them. With only 5 values there is no tail to worry about, which makes this a genuinely easy column to chart.

select queue,
       count(*)                                      as rows,
       round(100.0 * count(*) / sum(count(*)) over (), 1) as pct,
       round(avg(first_response_min)::numeric, 2)             as avg_first_response_min
from support_tickets
group by 1
order by rows desc;

The second column is the one that matters. Share of rows tells you what is common; avg_first_response_min tells you whether the common thing is the important thing. They disagree more often than not, and a chart that shows only the first is answering the easier question.

Decide what happens to the tail before you plot it. "Other" as an explicit bucket is honest; twelve slivers is not, and neither is silently taking the top eight.